Article Figures & Data

Figures

Visual stimulus and analysis overview. A white-and-black checkered sphere was displayed on a screen with a flickering rate of 6 Hz. The notations show the size of the sphere and the size of the field where the sphere could move. The position of the center of the sphere was predicted from measured brain activity. Prediction was performed based on maximum likelihood (ML) estimation using estimated receptive field models or the support vector regression algorithm.

Properties of estimated receptive field models. A, Histogram of the fitness of receptive field models. For each voxel, the correlation between the observed and fitted amplitudes was evaluated. Voxels were pooled across five subjects and three sessions. Voxels with estimated receptive field centers outside the field the stimulus could span were excluded. B, Mean receptive field size for each visual area. We evaluated the receptive field size of each voxel using the parameter sigma of the fitted Gaussian receptive field. Colored lines show the mean across voxels for individual subjects. Black line shows the mean across subjects. C, The relationship between eccentricity and receptive field size. The eccentricities of the estimated receptive field centers were binned into five levels with an interval of 1°. The mean receptive field size for each eccentricity level was calculated across voxels and plotted as a function of the eccentricity.

Position decoding accuracy from each visual area. A, Examples of true and predicted trajectories of the ball position. The predicted trajectories were produced by maximum likelihood estimation using the receptive field models. B, Decoding accuracy. The ball position was predicted from brain activity by maximum likelihood estimation with the RF models (upper) and SVR (lower). The accuracy was evaluated using the correlation coefficient between the true and predicted trajectories. The mean accuracies across subjects are shown. The calculations were performed separately for the horizontal (black line) and vertical (gray line) positions. Error bars show the 95% CIs across subjects.

Spatial distribution of estimated receptive fields. A, Examples of the distribution of estimated receptive field centers. Each circle shows the position of the receptive field center of a single voxel. We plotted the positions for the voxels in V1 and FFA from subject S3. B, Standard deviation of the positions of receptive field centers. The mean values across subjects are shown. Error bars show the 95% CIs across subjects.

Decoding accuracy after matching the numbers of voxels. A, Decoding accuracy with 20 voxels. The format is the same as in Fig. 3B. We performed decoding analysis with RF models on brain activity from 20 randomly selected voxels in each visual area. Decoding accuracies were first averaged across 100 instances of random voxel selection in individual subjects, and then averaged across subjects. Error bars show the 95% CIs across subjects. After matching the numbers of voxels, we observed a similar tendency as in Fig. 3B. This indicates that the tendency across visual areas was not caused by the difference in the number of voxels. B, The relationship between decoding accuracy and the number of voxels. Decoding analysis was performed with a fixed number of randomly selected voxels with the same procedure. Mean decoding accuracies were plotted as functions of the number of used voxels.

Decoding accuracy after excluding voxels whose receptive field centers are near the stimulus position. For each fMRI sample, we selected the receptive fields whose distances between the receptive field centers and the stimulus position were more than a threshold, and the stimulus position was predicted with those receptive fields by maximum likelihood estimation. The mean horizontal decoding accuracies across subjects for six visual areas are plotted as functions of the threshold. Error bars show the 95% CIs across subjects.